Cyanobacterial carboxysome mutant analysis reveals the influence of enzyme compartmentalization on cellular metabolism and metabolic network rigidity

Abstract

Cyanobacterial carboxysomes encapsulate carbonic anhydrase and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Genetic deletion of the major structural proteins encoded within the ccm operon in Synechococcus sp. PCC 7002 (ΔccmKLMN) disrupts carboxysome formation and significantly affects cellular physiology. Here we employed both metabolite pool size analysis and isotopically nonstationary metabolic flux analysis (INST-MFA) to examine metabolic regulation in cells lacking carboxysomes. Under high CO2 environments (1%), the ΔccmKLMN mutant could recover growth and had a similar central flux distribution as the control strain, with the exceptions of moderately decreased photosynthesis and elevated biomass protein content and photorespiration activity. Metabolite analyses indicated that the ΔccmKLMN strain had significantly larger pool sizes of pyruvate (> 18 folds), UDPG (uridine diphosphate glucose), and aspartate as well as higher levels of secreted organic acids (e.g., malate and succinate). These results suggest that the ΔccmKLMN mutant is able to largely maintain a fluxome similar to the control strain by changing in intracellular metabolite concentrations and metabolite overflows under optimal growth conditions. When CO2 was insufficient (0.2%), provision of acetate moderately promoted mutant growth. Interestingly, the removal of microcompartments may loosen the flux network and promote RuBisCO side-reactions, facilitating redirection of central metabolites to competing pathways (i.e., pyruvate to heterologous lactate production). This study provides important insights into metabolic regulation via enzyme compartmentation and cyanobacterial compensatory responses.

Quantifying Methane and Methanol Metabolism of “ Methylotuvimicrobium buryatense ” 5GB1C under Substrate Limitation

“…One contributing factor for lower 13 C enrichments in cyanobacteria could be the native metabolic channeling route located near carboxysomes, which confers metabolic efficiency but reduces cellular homogeneity. A recent study has shown that carboxysome-deficient mutants exhibit increased 13 C enrichments in central metabolites (25). In the absence of such a microcompartment, metabolites are more evenly distributed in M. buryatense 5GB1C, and overall, they seem to be labeled sequentially from the RuMP cycle to downstream pathways (Fig.…”

“…Since those changes are flux dependent, carbon fluxes can be calculated based on experimental measurements. 13 C INST-MFA has been applied to cyanobacteria and plants feeding on CO 2 as the sole carbon source (22–25), and it successfully captured their metabolic flux phenotypes in response to different growth conditions or genetic manipulations. However, it has not yet been used for any methanotroph growing on reduced one-carbon substrates.…”

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

“…A proposed explanation for this phenomenon is that CBB cycle metabolites may be subjected to metabolite channeling, a phenomenon where enzymes are organized into complexes that pass metabolites to each other, thereby potentially increasing the local concentration resulting in higher enzymatic reaction rates ( Süss et al, 1993 ; Anderson et al, 2005 ; Abernathy et al, 2017a ). The most obvious example of metabolite channeling in cyanobacteria occurs in the carboxysome, where RuBisCO and carbonic anhydrase are localized ( Yu et al, 1994 ; Rae et al, 2013 ; Long et al, 2018 ; Abernathy et al, 2019 ) to facilitate carbon fixation by raising the local concentration of CO 2 around RuBisCO.…”

Applications of stable isotope‐based metabolomics and fluxomics toward synthetic biology of cyanobacteria

“…Glycogen is often quantified using enzymatic hydrolysis followed by glucose analysis via HPLC or a colorimetric method (McKinlay, Shachar-Hill, Zeikus, & Vieille, 2007). Many of these methods have been successfully employed in cyanobacteria for biomass composition analysis (Abernathy et al, 2017(Abernathy et al, , 2019, but they are tedious and sometimes inaccurate. To this end, a single GC-MS method has been developed for E. coli to accurately and precisely quantify amino acids, fatty acids, RNA, and glycogen (Long & Antoniewicz, 2014).…”